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2022,31(5):1-20, DOI: 10.15888/j.cnki.csa.008463
Abstract:
Although the deep learning method has made a huge breakthrough in machine learning, it requires a large amount of manual work for data annotation. Limited by labor costs, however, many applications are expected to reason and judge the instance labels that have never been encountered before. For this reason, zero-shot learning (ZSL) came into being. As a natural data structure that represents the connection between things, the graph is currently drawing more and more attention in ZSL. Therefore, this study reviews the methods of graph-based ZSL systematically. Firstly, the definitions of ZSL and graph learning are outlined, and the ideas of existing solutions for ZSL are summarized. Secondly, the current ZSL methods are classified according to different utilization ways of graphs. Thirdly, the evaluation criteria and datasets concerning graph-based ZSL are discussed. Finally, this study also specifies the problems to be solved in further research on graph-based ZSL and predicts the possible directions of its future development.
Although the deep learning method has made a huge breakthrough in machine learning, it requires a large amount of manual work for data annotation. Limited by labor costs, however, many applications are expected to reason and judge the instance labels that have never been encountered before. For this reason, zero-shot learning (ZSL) came into being. As a natural data structure that represents the connection between things, the graph is currently drawing more and more attention in ZSL. Therefore, this study reviews the methods of graph-based ZSL systematically. Firstly, the definitions of ZSL and graph learning are outlined, and the ideas of existing solutions for ZSL are summarized. Secondly, the current ZSL methods are classified according to different utilization ways of graphs. Thirdly, the evaluation criteria and datasets concerning graph-based ZSL are discussed. Finally, this study also specifies the problems to be solved in further research on graph-based ZSL and predicts the possible directions of its future development.
2022,31(5):21-29, DOI: 10.15888/j.cnki.csa.008426
Abstract:
Operating systems play an important role in modern life. To serve different hardware environments and diverse application scenarios, they need to be scalable and flexible while maintaining good performance. A multicore operating system, as a kind of distributed operating system, is one of the solutions to this problem. This study analyzes the design principles of multicore operating systems, investigates existing multicore operating system technologies, and compares these technologies with other relevant ones. Finally, the current situation and trend of the research on multicore operating systems are summarized.
Operating systems play an important role in modern life. To serve different hardware environments and diverse application scenarios, they need to be scalable and flexible while maintaining good performance. A multicore operating system, as a kind of distributed operating system, is one of the solutions to this problem. This study analyzes the design principles of multicore operating systems, investigates existing multicore operating system technologies, and compares these technologies with other relevant ones. Finally, the current situation and trend of the research on multicore operating systems are summarized.
2022,31(5):30-39, DOI: 10.15888/j.cnki.csa.008467
Abstract:
Leaf vein segmentation is an important step in leaf pattern analysis, which is of great significance for soybean variety identification and phenotype research. On account of the complicated vein structure of soybean leaves and the low contrast of the leaf area where the veins are located, it is generally impossible to achieve ideal leaf vein segmentation results only using gray information. This study presents a soybean vein segmentation method combining the multi-scale gray unconstrained hit-or-miss transform (UHMT) algorithm and the processing method based on the hue data of HSI color space. In this method, the gray information in RGB color space and the hue data in HSI color space are used to segment the global leaf veins and local primary and secondary veins from soybean leaf images, respectively. The former uses iterative threshold segmentation to extract the leaf area and eliminates interference factors such as the outer contour and the petiole through expansion and corrosion to obtain the leaf area image. Then, the multi-scale gray UHMT algorithm is employed to obtain the global leaf vein image. Considering the poor performance of primary and secondary vein segmentation, we use hue data to enlarge the discrepancies in gray values between veins pixels and other pixels to realize the segmentation of local primary and secondary veins. The obtained global and local vein images are fused into the final soybean leaf vein image. Moreover, this study utilizes soybean leaf images in the soybean leaf image database, SoyCultivar, to verify the effectiveness of the algorithm. The results indicate that this algorithm is better than existing leaf vein segmentation methods as it can not only extract soybean leaf veins completely but also well eliminate the background, leaf contours, petioles, and other irrelevant components.
Leaf vein segmentation is an important step in leaf pattern analysis, which is of great significance for soybean variety identification and phenotype research. On account of the complicated vein structure of soybean leaves and the low contrast of the leaf area where the veins are located, it is generally impossible to achieve ideal leaf vein segmentation results only using gray information. This study presents a soybean vein segmentation method combining the multi-scale gray unconstrained hit-or-miss transform (UHMT) algorithm and the processing method based on the hue data of HSI color space. In this method, the gray information in RGB color space and the hue data in HSI color space are used to segment the global leaf veins and local primary and secondary veins from soybean leaf images, respectively. The former uses iterative threshold segmentation to extract the leaf area and eliminates interference factors such as the outer contour and the petiole through expansion and corrosion to obtain the leaf area image. Then, the multi-scale gray UHMT algorithm is employed to obtain the global leaf vein image. Considering the poor performance of primary and secondary vein segmentation, we use hue data to enlarge the discrepancies in gray values between veins pixels and other pixels to realize the segmentation of local primary and secondary veins. The obtained global and local vein images are fused into the final soybean leaf vein image. Moreover, this study utilizes soybean leaf images in the soybean leaf image database, SoyCultivar, to verify the effectiveness of the algorithm. The results indicate that this algorithm is better than existing leaf vein segmentation methods as it can not only extract soybean leaf veins completely but also well eliminate the background, leaf contours, petioles, and other irrelevant components.
2022,31(5):40-51, DOI: 10.15888/j.cnki.csa.008476
Abstract:
Single object tracking is a research focus in the field of computer vision. Traditional algorithms including correlation filtering have fast tracking speed but generally low tracking accuracy due to the roughness of extracted manual features such as color and gray levels. With the development of deep learning theory in recent years, tracking methods using deep features can achieve a good balance between tracking accuracy and speed. This study first introduces the relevant background of single object tracking and then sorts out multiple algorithms that have emerged in the development of single object tracking from the two stages of single object tracking based on correlation filters and deep learning. The current mainstream Siamese network algorithms are also introduced in detail. Finally, a large data set is used to compare and analyze the excellent algorithms that have emerged in recent years. In view of the shortcomings and deficiencies of these algorithms, the development prospects of this field are provided in this study.
Single object tracking is a research focus in the field of computer vision. Traditional algorithms including correlation filtering have fast tracking speed but generally low tracking accuracy due to the roughness of extracted manual features such as color and gray levels. With the development of deep learning theory in recent years, tracking methods using deep features can achieve a good balance between tracking accuracy and speed. This study first introduces the relevant background of single object tracking and then sorts out multiple algorithms that have emerged in the development of single object tracking from the two stages of single object tracking based on correlation filters and deep learning. The current mainstream Siamese network algorithms are also introduced in detail. Finally, a large data set is used to compare and analyze the excellent algorithms that have emerged in recent years. In view of the shortcomings and deficiencies of these algorithms, the development prospects of this field are provided in this study.
Improved Deep Neural Network with Overlapped Time Slice for Pattern Recognition of Motor Imagery EEG
2022,31(5):52-64, DOI: 10.15888/j.cnki.csa.008459
Abstract:
Pattern recognition of electroencephalogram (EEG) signals during motor imagery (MI) has been one of the most important research directions in the field of non-invasive brain-computer interface (BCI). In recent years, deep learning has further improved the recognition accuracy of EEG signals during MI. However, given the strong time variability of EEG signals, there are still some problems such as insufficient training samples and too high feature dimensions. To solve the above problems, this study proposes a new training strategy called “overlapped time slice”. Based on the existing cropped time slice strategy, this study adopts a novel overlapped time slice strategy and constructs a new loss function calculation and label prediction method with the overlapped time slice set. The overlapped time slice strategy can not only further increase the number of training samples but also reduce the feature space of a single sample to improve the performance of the deep neural network in EEG signal recognition. For the verification of the feasibility and effectiveness of the proposed overlapped strategy, three open-source EEG signal datasets, namely the BCI Competition IV datasets 1, 2a, and 2b, are selected in this study, and five kinds of deep neural network models are built on these three datasets. During experiments, the performance and efficiency of MI recognition are compared between the cropped strategy and the overlapped strategy. Experimental results show that the overlapped strategy has better recognition performance than that of the cropped strategy. Finally, nine groups of experiments are designed with different parameter combinations by adjusting the parameters of the overlapped time slice strategy. The experimental results demonstrate that parameter combination affects the final classification performance and that the classification performance is not in a linear relationship with the efficiency. The recognition accuracy of the proposed overlapped strategy on dataset 1, 2a, and 2b is 92.3%, 77.8%, and 86.3% respectively. Compared with the conventional cropped strategy, the proposed overlapped strategy has improved the performance significantly without necessarily reducing the efficiency.
Pattern recognition of electroencephalogram (EEG) signals during motor imagery (MI) has been one of the most important research directions in the field of non-invasive brain-computer interface (BCI). In recent years, deep learning has further improved the recognition accuracy of EEG signals during MI. However, given the strong time variability of EEG signals, there are still some problems such as insufficient training samples and too high feature dimensions. To solve the above problems, this study proposes a new training strategy called “overlapped time slice”. Based on the existing cropped time slice strategy, this study adopts a novel overlapped time slice strategy and constructs a new loss function calculation and label prediction method with the overlapped time slice set. The overlapped time slice strategy can not only further increase the number of training samples but also reduce the feature space of a single sample to improve the performance of the deep neural network in EEG signal recognition. For the verification of the feasibility and effectiveness of the proposed overlapped strategy, three open-source EEG signal datasets, namely the BCI Competition IV datasets 1, 2a, and 2b, are selected in this study, and five kinds of deep neural network models are built on these three datasets. During experiments, the performance and efficiency of MI recognition are compared between the cropped strategy and the overlapped strategy. Experimental results show that the overlapped strategy has better recognition performance than that of the cropped strategy. Finally, nine groups of experiments are designed with different parameter combinations by adjusting the parameters of the overlapped time slice strategy. The experimental results demonstrate that parameter combination affects the final classification performance and that the classification performance is not in a linear relationship with the efficiency. The recognition accuracy of the proposed overlapped strategy on dataset 1, 2a, and 2b is 92.3%, 77.8%, and 86.3% respectively. Compared with the conventional cropped strategy, the proposed overlapped strategy has improved the performance significantly without necessarily reducing the efficiency.
2022,31(5):65-74, DOI: 10.15888/j.cnki.csa.008550
Abstract:
With the development of science and technology, the deployment of large-scale quantum computers is becoming possible, and the public-key cryptographic algorithms based on some difficult problems will be solved by quantum algorithms effectively. The security of traditional trusted hardware chips such as TCM/TPM will be seriously affected due to the wide use of public-key cryptosystems such as RSA, SM3, and ECC, and most of the quantum-resistant (QR) cryptographic algorithms cannot be implemented on hardware chips with limited computational resources. Therefore, it is necessary to redesign the QR trusted computing platform. In this study, considering the security challenges faced by trusted computing in quantum computing models, we summarize the present situation of QR trusted computing research and propose a QR trusted computing technology system. Combined with the existing post-quantum cryptographic protocol and trusted computing software and hardware technology framework, we transplant the QR cryptographic algorithms and protocol on the trusted computing platform and implement a prototype system of a QR trusted computing security support platform based on TCM. The work includes the design of the primitive root key and QR extensions such as TCM cipher library, remote attestation, and LDAA. Finally, the results of function and performance tests on the emulator for the above TCM modules show that the prototype system is resistant to attacks by quantum algorithms, with acceptable application performance overhead.
With the development of science and technology, the deployment of large-scale quantum computers is becoming possible, and the public-key cryptographic algorithms based on some difficult problems will be solved by quantum algorithms effectively. The security of traditional trusted hardware chips such as TCM/TPM will be seriously affected due to the wide use of public-key cryptosystems such as RSA, SM3, and ECC, and most of the quantum-resistant (QR) cryptographic algorithms cannot be implemented on hardware chips with limited computational resources. Therefore, it is necessary to redesign the QR trusted computing platform. In this study, considering the security challenges faced by trusted computing in quantum computing models, we summarize the present situation of QR trusted computing research and propose a QR trusted computing technology system. Combined with the existing post-quantum cryptographic protocol and trusted computing software and hardware technology framework, we transplant the QR cryptographic algorithms and protocol on the trusted computing platform and implement a prototype system of a QR trusted computing security support platform based on TCM. The work includes the design of the primitive root key and QR extensions such as TCM cipher library, remote attestation, and LDAA. Finally, the results of function and performance tests on the emulator for the above TCM modules show that the prototype system is resistant to attacks by quantum algorithms, with acceptable application performance overhead.
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Available online: May 17, 2022 , DOI: 10.15888/j.cnki.csa.008544
Abstract:
Text matching is a core research area in natural language processing. Deep text matching models can be broadly classified into representational models and interactive models. The former tends to lose semantic focus and fails to measure the contextual importance of words. The latter lacks global information such as sentence type and inter-sentence information. To address these problems, we propose a text matching model incorporating multi-angle features based on Siamese neural network. The model generates word vectors using the BERT model and enhances semantic features by the similarity fusion of words. It then encodes the syntactic structured features using Bi-LSTM, namely the syntactic structured information containing the lexical sequence. A Transformer encoder is utilized to realize the multi-level interaction between the syntactic structured features and the text features. Finally, the similarity is deduced by spliced vectors. Experiments on part of Quora question pair show that this model performs better than the classical deep matching model.
Text matching is a core research area in natural language processing. Deep text matching models can be broadly classified into representational models and interactive models. The former tends to lose semantic focus and fails to measure the contextual importance of words. The latter lacks global information such as sentence type and inter-sentence information. To address these problems, we propose a text matching model incorporating multi-angle features based on Siamese neural network. The model generates word vectors using the BERT model and enhances semantic features by the similarity fusion of words. It then encodes the syntactic structured features using Bi-LSTM, namely the syntactic structured information containing the lexical sequence. A Transformer encoder is utilized to realize the multi-level interaction between the syntactic structured features and the text features. Finally, the similarity is deduced by spliced vectors. Experiments on part of Quora question pair show that this model performs better than the classical deep matching model.
Available online: May 17, 2022 , DOI: 10.15888/j.cnki.csa.008549
Abstract:
Automatic recognition of crop leaf diseases is an important application of computer vision technology in agriculture. In recent years, deep learning methods have made some progress in the recognition of crop leaf diseases, and they are all based on deep feature representations of a single deep convolutional neural network (CNN) model. However, the useful fact that the image representation ability of different deep CNN models is complementary has not received attention for research. Thus, this study proposes a network model MDFF-Net for fusing different deep features. MDFF-Net connects two pre-trained deep CNN models in parallel and then sets a fully connected layer with the same number of neurons for each model to transform the deep features output by different models into features with the same dimension. Then, through the non-linear transform of two fully connected layers, the effect of feature fusion is further improved. We choose VGG-16 and ResNet-50 as the feature extractors of MDFF-Net and conduct experiments on a public dataset containing five apple leaf diseases. The experimental results show that the recognition accuracy of MDFF-Net is 96.59%, which is better than the results achieved by VGG-16 or ResNet-50 alone and thus proves the effectiveness of the deep feature fusion method.
Automatic recognition of crop leaf diseases is an important application of computer vision technology in agriculture. In recent years, deep learning methods have made some progress in the recognition of crop leaf diseases, and they are all based on deep feature representations of a single deep convolutional neural network (CNN) model. However, the useful fact that the image representation ability of different deep CNN models is complementary has not received attention for research. Thus, this study proposes a network model MDFF-Net for fusing different deep features. MDFF-Net connects two pre-trained deep CNN models in parallel and then sets a fully connected layer with the same number of neurons for each model to transform the deep features output by different models into features with the same dimension. Then, through the non-linear transform of two fully connected layers, the effect of feature fusion is further improved. We choose VGG-16 and ResNet-50 as the feature extractors of MDFF-Net and conduct experiments on a public dataset containing five apple leaf diseases. The experimental results show that the recognition accuracy of MDFF-Net is 96.59%, which is better than the results achieved by VGG-16 or ResNet-50 alone and thus proves the effectiveness of the deep feature fusion method.
Available online: May 17, 2022 , DOI: 10.15888/j.cnki.csa.008559
Abstract:
In the process of sinter production, the pores formed in the sinter are an important parameter for sinter quality evaluation. Due to the different shapes of sinter pores and their fuzzy edge, the error rates of pores segmented are large. In order that the pores can be segmented more accurately, the sinter image is preprocessed by OpenCV. Through a comparative analysis of the traditional image segmentation algorithm, this study proposes an algorithm based on an improved UNet network to segment the preprocessed sinter pore image. An improved module integrating residual and splicing is introduced into UNet network coding to obtain more information on pore features. The experimental results show that the improved algorithm is better than both the traditional UNet network and the traditional image segmentation algorithm in mean intersection over union (MIoU) and the Dice index.
In the process of sinter production, the pores formed in the sinter are an important parameter for sinter quality evaluation. Due to the different shapes of sinter pores and their fuzzy edge, the error rates of pores segmented are large. In order that the pores can be segmented more accurately, the sinter image is preprocessed by OpenCV. Through a comparative analysis of the traditional image segmentation algorithm, this study proposes an algorithm based on an improved UNet network to segment the preprocessed sinter pore image. An improved module integrating residual and splicing is introduced into UNet network coding to obtain more information on pore features. The experimental results show that the improved algorithm is better than both the traditional UNet network and the traditional image segmentation algorithm in mean intersection over union (MIoU) and the Dice index.
Available online: April 18, 2022 , DOI: 10.15888/j.cnki.csa.008607
Abstract:
For finding the ophthalmic diseases that can be observed from retinal vessels, fundus images play a key role and provide an effective reference for professional medical personnel. However, manual vessel segmentation has a large workload, which is time-consuming and laborious. Therefore, developing an automatic and intelligent segmentation method is of great benefit to relevant personnel. In this study, the attention mechanism and RU-Net structure are integrated into the generator of generative adversarial networks (GANs), forming a new structure—Retina-GAN. At the same time, automatic color equalization (ACE) is selected in the preprocessing of fundus images to improve image contrast and make blood vessels clearer. To validate the proposed approach, we compared the Retina-GAN with some other models on DRIVE datasets. Accuracy, sensitivity, and specificity are measured for comparative analysis. The experiment shows that Retina-GAN has better performance than other models.
For finding the ophthalmic diseases that can be observed from retinal vessels, fundus images play a key role and provide an effective reference for professional medical personnel. However, manual vessel segmentation has a large workload, which is time-consuming and laborious. Therefore, developing an automatic and intelligent segmentation method is of great benefit to relevant personnel. In this study, the attention mechanism and RU-Net structure are integrated into the generator of generative adversarial networks (GANs), forming a new structure—Retina-GAN. At the same time, automatic color equalization (ACE) is selected in the preprocessing of fundus images to improve image contrast and make blood vessels clearer. To validate the proposed approach, we compared the Retina-GAN with some other models on DRIVE datasets. Accuracy, sensitivity, and specificity are measured for comparative analysis. The experiment shows that Retina-GAN has better performance than other models.
Available online: April 18, 2022 , DOI: 10.15888/j.cnki.csa.008608
Abstract:
The video magnification technology gives people an opportunity to observe and study small changes in things. A video is decomposed by the complex steerable pyramid and the motion in the video is manipulated by analyzing the phase differences at different scales and in different directions. Magnified signals at some scales in the complex steerable pyramid exceed their phase shift limits, resulting in artifacts and blurring. This problem can be solved by adjusting the magnification factor for each scale. In this paper, a video magnification algorithm based on multi-scale filtering is proposed. A relation between the spatial wavelength of a video frame image and vibration displacement is established to determine the upper limit of the magnification factor for each scale. The manual setting of a cut-off wavelength is no longer necessary, and the amplification factor for each scale in the complex steerable pyramid is adjusted automatically so that magnified signals can adapt to their phase shift limits. The vibration attached to the large motion is magnified to verify the performance of the proposed algorithm, and the result shows that it has obvious advantages over the existing video amplification algorithms.
The video magnification technology gives people an opportunity to observe and study small changes in things. A video is decomposed by the complex steerable pyramid and the motion in the video is manipulated by analyzing the phase differences at different scales and in different directions. Magnified signals at some scales in the complex steerable pyramid exceed their phase shift limits, resulting in artifacts and blurring. This problem can be solved by adjusting the magnification factor for each scale. In this paper, a video magnification algorithm based on multi-scale filtering is proposed. A relation between the spatial wavelength of a video frame image and vibration displacement is established to determine the upper limit of the magnification factor for each scale. The manual setting of a cut-off wavelength is no longer necessary, and the amplification factor for each scale in the complex steerable pyramid is adjusted automatically so that magnified signals can adapt to their phase shift limits. The vibration attached to the large motion is magnified to verify the performance of the proposed algorithm, and the result shows that it has obvious advantages over the existing video amplification algorithms.
Available online: April 18, 2022 , DOI: 10.15888/j.cnki.csa.008609
Abstract:
Compared with other fine-grained image classifications, that of wild snakes is more difficult and complicated, as it is difficult to judge and classify snakes by their local characteristics due to their different postures, rapid posture changes, and usual status of motion or coiling. In response, this paper applies the self-attention mechanism to fine-grained wild snake image classification to solve the problem that the convolutional neural network focuses too much on the local parts to ignore the global information due to the increasing number of layers. Transfer learning is implemented through Swin Transformer (Swin-T) to obtain a fine-grained feature extraction model. To further study the performance of the self-attention mechanism in meta-learning, this paper improves the feature extraction model, builds a Siamese network, and construct a meta-learner to learn and classify a small number of samples. Compared with other methods, the proposed method reduces the time and space consumption caused by feature extraction, improves the accuracy and efficiency of meta-learning classification, and increases the learning autonomy of meta-learning.
Compared with other fine-grained image classifications, that of wild snakes is more difficult and complicated, as it is difficult to judge and classify snakes by their local characteristics due to their different postures, rapid posture changes, and usual status of motion or coiling. In response, this paper applies the self-attention mechanism to fine-grained wild snake image classification to solve the problem that the convolutional neural network focuses too much on the local parts to ignore the global information due to the increasing number of layers. Transfer learning is implemented through Swin Transformer (Swin-T) to obtain a fine-grained feature extraction model. To further study the performance of the self-attention mechanism in meta-learning, this paper improves the feature extraction model, builds a Siamese network, and construct a meta-learner to learn and classify a small number of samples. Compared with other methods, the proposed method reduces the time and space consumption caused by feature extraction, improves the accuracy and efficiency of meta-learning classification, and increases the learning autonomy of meta-learning.
Available online: April 18, 2022 , DOI: 10.15888/j.cnki.csa.008610
Abstract:
To address the cold-start and sparsity problems of recommendation systems, this paper proposes a recommendation model based on a heterogeneous information network. Previous approaches were unable to take into account both knowledge graph representation learning and implicit path information, which made the performance of knowledge recommendation systems mediocre. The proposed method sets meta-paths in the heterogeneous information network and integrates them into knowledge graph representation learning by the graph neural network (GNN). Next, the attention network is used to connect a recommendation task with a knowledge graph representation task. It can not only learn the potential features of the two tasks but also enhance the interactions between the recommended items in the recommendation system and the entities in the knowledge graph. Finally, the user click rate is predicted in the recommendation task. The method is experimented on the open dataset Book-Crossing and the knowledge graph constructed with the DBLP dataset, and the results demonstrate that the proposed model achieves better performance than that of other algorithms in indexes of area under curve (AUC), recall, and F1-score.
To address the cold-start and sparsity problems of recommendation systems, this paper proposes a recommendation model based on a heterogeneous information network. Previous approaches were unable to take into account both knowledge graph representation learning and implicit path information, which made the performance of knowledge recommendation systems mediocre. The proposed method sets meta-paths in the heterogeneous information network and integrates them into knowledge graph representation learning by the graph neural network (GNN). Next, the attention network is used to connect a recommendation task with a knowledge graph representation task. It can not only learn the potential features of the two tasks but also enhance the interactions between the recommended items in the recommendation system and the entities in the knowledge graph. Finally, the user click rate is predicted in the recommendation task. The method is experimented on the open dataset Book-Crossing and the knowledge graph constructed with the DBLP dataset, and the results demonstrate that the proposed model achieves better performance than that of other algorithms in indexes of area under curve (AUC), recall, and F1-score.
Available online: April 18, 2022 , DOI: 10.15888/j.cnki.csa.008612
Abstract:
YOLOx-Darknet53 is an improved detection network integrating a basis of you only look once version 3 (YOLOv3) with various tricks added. Nevertheless, it still uses Darknet53 as the backbone network to extract features, so the feature extraction capability of the network is still insufficient. In this paper, we acquire a contextual attention (CoA) module by improving the attention mechanism in CoTNet and replace the 3×3 convolution in the residual block of the YOLOx backbone network with the module to obtain a new residual block after attention fusion and thereby strengthen the feature extraction capability of the backbone network. A comparison experiment is conducted on the Pascal VOC2007 data set. The mean average precision AP@[.5:.95] and the AP@0.5 of the network integrating the CoA module are both 1.4 higher than those of the original network. After the backbone network is improved, a non-parameter 3D attention module is added in front of the YOLOx detection head to obtain the final improved detection network. The results of another round of the above comparative experiment show that the AP@[.5:.95] and the AP@0.5 of the final network are respectively 1.6 and 1.5 higher than those of the original network. Therefore, the improved network is more accurate than the original network in detection and can achieve better detection effects in industrial applications.
YOLOx-Darknet53 is an improved detection network integrating a basis of you only look once version 3 (YOLOv3) with various tricks added. Nevertheless, it still uses Darknet53 as the backbone network to extract features, so the feature extraction capability of the network is still insufficient. In this paper, we acquire a contextual attention (CoA) module by improving the attention mechanism in CoTNet and replace the 3×3 convolution in the residual block of the YOLOx backbone network with the module to obtain a new residual block after attention fusion and thereby strengthen the feature extraction capability of the backbone network. A comparison experiment is conducted on the Pascal VOC2007 data set. The mean average precision AP@[.5:.95] and the AP@0.5 of the network integrating the CoA module are both 1.4 higher than those of the original network. After the backbone network is improved, a non-parameter 3D attention module is added in front of the YOLOx detection head to obtain the final improved detection network. The results of another round of the above comparative experiment show that the AP@[.5:.95] and the AP@0.5 of the final network are respectively 1.6 and 1.5 higher than those of the original network. Therefore, the improved network is more accurate than the original network in detection and can achieve better detection effects in industrial applications.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008601
Abstract:
This study designs and develops a representation and search method and a relevant system for dynamic linked data networks. The method can help users obtain the association around an entity when there are complicated entities and associations, and the network can be dynamically expanded through guided interaction. When users know multiple entities which are suspected to be associated, the minimum connected graph algorithm based on distributed computing is used to search out the association network. Application cases show that the proposed method and system can achieve good results and user experiences, and the system based on the method has been applied in many engineering projects such as Smart City, Safe City and Metropolitan IoT.
This study designs and develops a representation and search method and a relevant system for dynamic linked data networks. The method can help users obtain the association around an entity when there are complicated entities and associations, and the network can be dynamically expanded through guided interaction. When users know multiple entities which are suspected to be associated, the minimum connected graph algorithm based on distributed computing is used to search out the association network. Application cases show that the proposed method and system can achieve good results and user experiences, and the system based on the method has been applied in many engineering projects such as Smart City, Safe City and Metropolitan IoT.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008602
Abstract:
To accurately classify Sina microblog comment information, this study proposes an improved genetic algorithm-improved particle swarm optimization-balanced support vector machine (GA-IPSO-BSVM) classification model to enhance the accuracy and convergence of classifying Sina microblog comment information. Firstly, to effectively improve the algorithm convergence speed and efficiently save computational resources, this model introduces the elimination mechanism of the GA in the early iteration to remove a large number of low-speed particles. Secondly, to avoid the algorithm being trapped in local optima and improve the topology of particle relations in PSO, this study utilizes a K-means clustering algorithm to perform cluster partition of particle swarms in the middle of the iteration. The particle swarms are iterated in the communities and excellent particles are selected in each community. Thirdly, all excellent particles in the communities are combined into an excellent particle swarm that is iterated to derive the global optimal solution in the late iteration. Fourthly, the hyperparameter optimization of BSVM is performed by combining GA with IPSO to enhance classification accuracy. Finally, the proposed GA-IPSO-BSVM model is used for verifying the classification and prediction of Sina microblog comment information. The experimental results demonstrate the superiority of our proposed classification model over other benchmark models applied to Sina microblog comment information classification in terms of accuracy improvement.
To accurately classify Sina microblog comment information, this study proposes an improved genetic algorithm-improved particle swarm optimization-balanced support vector machine (GA-IPSO-BSVM) classification model to enhance the accuracy and convergence of classifying Sina microblog comment information. Firstly, to effectively improve the algorithm convergence speed and efficiently save computational resources, this model introduces the elimination mechanism of the GA in the early iteration to remove a large number of low-speed particles. Secondly, to avoid the algorithm being trapped in local optima and improve the topology of particle relations in PSO, this study utilizes a K-means clustering algorithm to perform cluster partition of particle swarms in the middle of the iteration. The particle swarms are iterated in the communities and excellent particles are selected in each community. Thirdly, all excellent particles in the communities are combined into an excellent particle swarm that is iterated to derive the global optimal solution in the late iteration. Fourthly, the hyperparameter optimization of BSVM is performed by combining GA with IPSO to enhance classification accuracy. Finally, the proposed GA-IPSO-BSVM model is used for verifying the classification and prediction of Sina microblog comment information. The experimental results demonstrate the superiority of our proposed classification model over other benchmark models applied to Sina microblog comment information classification in terms of accuracy improvement.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008603
Abstract:
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008604
Abstract:
The pupil distance of human eyes is a basic parameter which needs to be measured accurately in optometry research and glasses prescription. It is of great application value to realize automatic pupil distance detection. With the help of a Gaussian skin color model, this study proposes a method of pupil location and pupil distance calculation based on gray integral projection and Hough circle transform. Firstly, the image is subjected to the preprocessing of illumination compensation by the adaptive brightness correction method of a two-dimensional gamma function. On this basis, the face area is extracted by using a skin color probability model. Then, the gray integral projection method is used to extract the eye area by selecting an appropriate threshold. After morphological image processing, the Canny edge detection operator is utilized to detect the pupil edge. Finally, the pupil location is carried out by Hough circle transform, and the center distance is calculated. The results show that the algorithm has a fast pupil location speed and can realize the automatic pupil distance measurement more accurately, which lays the foundation for the research on intelligent technologies of pupil distance measurement.
The pupil distance of human eyes is a basic parameter which needs to be measured accurately in optometry research and glasses prescription. It is of great application value to realize automatic pupil distance detection. With the help of a Gaussian skin color model, this study proposes a method of pupil location and pupil distance calculation based on gray integral projection and Hough circle transform. Firstly, the image is subjected to the preprocessing of illumination compensation by the adaptive brightness correction method of a two-dimensional gamma function. On this basis, the face area is extracted by using a skin color probability model. Then, the gray integral projection method is used to extract the eye area by selecting an appropriate threshold. After morphological image processing, the Canny edge detection operator is utilized to detect the pupil edge. Finally, the pupil location is carried out by Hough circle transform, and the center distance is calculated. The results show that the algorithm has a fast pupil location speed and can realize the automatic pupil distance measurement more accurately, which lays the foundation for the research on intelligent technologies of pupil distance measurement.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008605
Abstract:
Ensemble learning has been widely used for improving classification accuracy. Recent studies show that building ensemble classifiers through a multi-modal perturbation strategy can further improve classification performance. In this study, we propose an ensemble pruning algorithm based on approximate reducts and optimal sampling (EPA_AO). In EPA_AO, we design the multi-modal perturbation strategy to build different individual classifiers. The proposed perturbation strategy can simultaneously perturb the attribute space and training set, which can improve the diversity of individual classifiers. We use the evidential K-nearest neighbor (KNN) algorithm to train individual classifiers and compare EPA_AO with existing algorithms of the same type on multiple UCI data sets. Experimental results show that EPA_AO is an effective ensemble learning approach.
Ensemble learning has been widely used for improving classification accuracy. Recent studies show that building ensemble classifiers through a multi-modal perturbation strategy can further improve classification performance. In this study, we propose an ensemble pruning algorithm based on approximate reducts and optimal sampling (EPA_AO). In EPA_AO, we design the multi-modal perturbation strategy to build different individual classifiers. The proposed perturbation strategy can simultaneously perturb the attribute space and training set, which can improve the diversity of individual classifiers. We use the evidential K-nearest neighbor (KNN) algorithm to train individual classifiers and compare EPA_AO with existing algorithms of the same type on multiple UCI data sets. Experimental results show that EPA_AO is an effective ensemble learning approach.
Available online: March 24, 2022 , DOI: 10.15888/j.cnki.csa.008561
Abstract:
On a construction site, safety helmets can reduce head injuries, and safety helmets of different colors represent different identities. The contemporary method of detecting safety helmet wearing and identifying types of work by video surveillance is time-consuming, incomplete, and low in supervision efficiency. In response, this study proposes an improved method of safety helmet wearing detection and identity recognition based on the you only look once version 4 (YOLOv4). On the basis of the original YOLOv4, the K-means algorithm is used to cluster the size of the prior box again, and multi-scale prediction output is added. The experimental distance intersection over union–non-maximum suppression (DIoU–NMS) is used for NMS so that safety helmet wearing detection and identity recognition of workers can achieve high efficiency and comprehensiveness. The results show that the average detection accuracy among workers wearing red, blue, yellow, and white safety helmets and workers without safety helmets is 92.1%, which means the proposed method ensures the real-time monitoring of the safety helmet wearing of workers on the construction site.
On a construction site, safety helmets can reduce head injuries, and safety helmets of different colors represent different identities. The contemporary method of detecting safety helmet wearing and identifying types of work by video surveillance is time-consuming, incomplete, and low in supervision efficiency. In response, this study proposes an improved method of safety helmet wearing detection and identity recognition based on the you only look once version 4 (YOLOv4). On the basis of the original YOLOv4, the K-means algorithm is used to cluster the size of the prior box again, and multi-scale prediction output is added. The experimental distance intersection over union–non-maximum suppression (DIoU–NMS) is used for NMS so that safety helmet wearing detection and identity recognition of workers can achieve high efficiency and comprehensiveness. The results show that the average detection accuracy among workers wearing red, blue, yellow, and white safety helmets and workers without safety helmets is 92.1%, which means the proposed method ensures the real-time monitoring of the safety helmet wearing of workers on the construction site.
Available online: March 18, 2022 , DOI: 10.15888/j.cnki.csa.008575
Abstract:
To improve the robustness of the digital watermarking algorithm, this study proposes a zero-watermarking algorithm for three-dimensional (3D) mesh based on the vector length ratio. First, the 3D mesh model is converted from a rectangular coordinate system to a spherical coordinate system. Second, we divide the vertices of the mesh model into blocks and calculate the centroid of each block. Then, we calculate the ratio of the distance between each vertex and the centroid of the block to the average distance between its neighbor vertex and the centroid and compare the ratio sequence with the average of the ratios. We record the comparison results as 0 or 1 according to the size relation and perform statistics of the majority of the numbers of 0 and 1 in each block. In this way, the characteristic sequence of the model can be constituted. Finally, the binary sequence of the copyright information and the characteristic sequence are XORed to obtain the final zero-watermark sequence. Experimental results show that the algorithm in this study can resist common attacks such as translation, rotation, uniform scaling, noise, smoothing, simplification, and cropping, and can protect the copyright of the 3D mesh model.
To improve the robustness of the digital watermarking algorithm, this study proposes a zero-watermarking algorithm for three-dimensional (3D) mesh based on the vector length ratio. First, the 3D mesh model is converted from a rectangular coordinate system to a spherical coordinate system. Second, we divide the vertices of the mesh model into blocks and calculate the centroid of each block. Then, we calculate the ratio of the distance between each vertex and the centroid of the block to the average distance between its neighbor vertex and the centroid and compare the ratio sequence with the average of the ratios. We record the comparison results as 0 or 1 according to the size relation and perform statistics of the majority of the numbers of 0 and 1 in each block. In this way, the characteristic sequence of the model can be constituted. Finally, the binary sequence of the copyright information and the characteristic sequence are XORed to obtain the final zero-watermark sequence. Experimental results show that the algorithm in this study can resist common attacks such as translation, rotation, uniform scaling, noise, smoothing, simplification, and cropping, and can protect the copyright of the 3D mesh model.
Available online: March 18, 2022 , DOI: 10.15888/j.cnki.csa.008576
Abstract:
In recent years, artificial intelligence has been widely used in various fields. To address time-consuming manual weighing and complicated pricing procedures in supermarkets and vegetable markets, this study proposes an automatic fruit recognition model based on attention YOLOv5. First, to improve the recognition accuracy of fruits with different local features but similar global features, the study adds squeeze-and-excitation networks (SENet) after the spatial pyramid pooling (SPP) layer of YOLOv5 and uses the attention mechanism to automatically learn the importance of each feature channel. Further, the useful features for fruit recognition tasks according to the importance are strengthened and those useless are suppressed. Second, when the fruit recognition prediction frame overlaps the target frame, GIOU cannot accurately express the overlapping relationship of the frames. In response, this study replaces the original frame regression loss function GIOU with CIOU and considers the relationships of aspect ratio and center point between the target frame and the prediction frame. In this way, the fruit prediction frame is closer to the real frame, and thereby the prediction accuracy is improved. Experimental results show that the improved model has significantly improved fruit recognition ability in common scenarios with a mean average precision (mAP) of 99.10% and a recognition speed of 82 FPS, which can meet the needs of practical applications.
In recent years, artificial intelligence has been widely used in various fields. To address time-consuming manual weighing and complicated pricing procedures in supermarkets and vegetable markets, this study proposes an automatic fruit recognition model based on attention YOLOv5. First, to improve the recognition accuracy of fruits with different local features but similar global features, the study adds squeeze-and-excitation networks (SENet) after the spatial pyramid pooling (SPP) layer of YOLOv5 and uses the attention mechanism to automatically learn the importance of each feature channel. Further, the useful features for fruit recognition tasks according to the importance are strengthened and those useless are suppressed. Second, when the fruit recognition prediction frame overlaps the target frame, GIOU cannot accurately express the overlapping relationship of the frames. In response, this study replaces the original frame regression loss function GIOU with CIOU and considers the relationships of aspect ratio and center point between the target frame and the prediction frame. In this way, the fruit prediction frame is closer to the real frame, and thereby the prediction accuracy is improved. Experimental results show that the improved model has significantly improved fruit recognition ability in common scenarios with a mean average precision (mAP) of 99.10% and a recognition speed of 82 FPS, which can meet the needs of practical applications.
Available online: March 18, 2022 , DOI: 10.15888/j.cnki.csa.008592
Abstract:
There is a large amount of failure information from the engine after-sales maintenance and failure reports. This study introduces knowledge graphs and designs a systematic building procedure for the field of engine fault. It carries out ontology modeling for the multi-source fault data. The entity recognition framework that combines BERT with BiLSTM-CRF is used to mine expert knowledge in fault data. The index FF-IEF (fault frequency-inverse event frequency) is proposed, and fault diagnosis is performed based on the knowledge graph and Bayesian network. We design and develop the prototype system EFKG that contains12534 entities and 408972 triplets. The system provides knowledge extraction, visual retrieval, and auxiliary decision-making. It can effectively improve the efficiency of information retrieval and maintenance and is of guiding significance for the application of knowledge graphs in the field of engine fault.
There is a large amount of failure information from the engine after-sales maintenance and failure reports. This study introduces knowledge graphs and designs a systematic building procedure for the field of engine fault. It carries out ontology modeling for the multi-source fault data. The entity recognition framework that combines BERT with BiLSTM-CRF is used to mine expert knowledge in fault data. The index FF-IEF (fault frequency-inverse event frequency) is proposed, and fault diagnosis is performed based on the knowledge graph and Bayesian network. We design and develop the prototype system EFKG that contains
Available online: March 09, 2022 , DOI: 10.15888/j.cnki.csa.008593
Abstract:
In recent years, the application of scene graphs in image captioning has been increasingly researched. However, the current image captioning models based on scene graphs cannot take into account the previous input retained in long short-term memory (LSTM) networks, which may lead to missed information. In this study, we firstly propose the image captioning network based on original information injection, which keeps the original input information as much as possible and reduces the missed information. Secondly, we consider that the degree of the current graph updating mechanism is too large, which may lead to the missing of node information. Thus, we propose a visit control module to update the weights of visited nodes, avoiding such missing. Finally, we design a graph update factor (GUF) to determine the update level. We conduct experiments on the official dataset: MSCOCO. The mechanism evaluation shows that our model has achieved more competitive results compared with the baseline model.
In recent years, the application of scene graphs in image captioning has been increasingly researched. However, the current image captioning models based on scene graphs cannot take into account the previous input retained in long short-term memory (LSTM) networks, which may lead to missed information. In this study, we firstly propose the image captioning network based on original information injection, which keeps the original input information as much as possible and reduces the missed information. Secondly, we consider that the degree of the current graph updating mechanism is too large, which may lead to the missing of node information. Thus, we propose a visit control module to update the weights of visited nodes, avoiding such missing. Finally, we design a graph update factor (GUF) to determine the update level. We conduct experiments on the official dataset: MSCOCO. The mechanism evaluation shows that our model has achieved more competitive results compared with the baseline model.
Available online: March 09, 2022 , DOI: 10.15888/j.cnki.csa.008594
Abstract:
Given the low recognition rate and the difficulty in detecting small cracks in the asphalt pavement under complex background, the crack detection method based on improved Faster-RCNN is proposed. First, the road surface images are collected by the multifunctional road detection vehicle, and 13 000 pictures are divided into training sets and test sets at a ratio of 8:2. Then VGG16, MobileNet-V2, and ResNet50 networks are utilized to replace the feature extraction network in the Faster-RCNN model to identify the cracks. The results show that the combination of ResNet50 and Faster-RCNN can achieve the best result with an accuracy of 0.805 8. The cracks are distributed on the same level without hierarchical information. Therefore, other ResNet networks are expected to work better with the Faster-RCNN model. However, it turns out that ResNet50 still outperforms ResNet18 and ResNet101. In the case of missed detection of small cracks, the convolutional block attention module (CBAM) module is also introduced into ResNet50 and the influence of different insertion positions on the detection accuracy is compared. Experiments show that the improved Faster-RCNN model has a detection accuracy of 85.64%, which can effectively detect small cracks under complex backgrounds.
Given the low recognition rate and the difficulty in detecting small cracks in the asphalt pavement under complex background, the crack detection method based on improved Faster-RCNN is proposed. First, the road surface images are collected by the multifunctional road detection vehicle, and 13 000 pictures are divided into training sets and test sets at a ratio of 8:2. Then VGG16, MobileNet-V2, and ResNet50 networks are utilized to replace the feature extraction network in the Faster-RCNN model to identify the cracks. The results show that the combination of ResNet50 and Faster-RCNN can achieve the best result with an accuracy of 0.805 8. The cracks are distributed on the same level without hierarchical information. Therefore, other ResNet networks are expected to work better with the Faster-RCNN model. However, it turns out that ResNet50 still outperforms ResNet18 and ResNet101. In the case of missed detection of small cracks, the convolutional block attention module (CBAM) module is also introduced into ResNet50 and the influence of different insertion positions on the detection accuracy is compared. Experiments show that the improved Faster-RCNN model has a detection accuracy of 85.64%, which can effectively detect small cracks under complex backgrounds.
LSCN Infrared Image Enhancement Based on Non-separable Wavelet Based Multiscale Directional Analysis
Available online: March 09, 2022 , DOI: 10.15888/j.cnki.csa.008578
Abstract:
To address the low signal-to-noise ratio of infrared images and their vulnerability to the impact of the background environment, this study proposes an image enhancement algorithm based on non-separable wavelet based multiscale directional analysis (NSWMDA) and linking synaptic computation network (LSCN). Firstly, the original image is subjected to non-separable wavelet transform (NSWT) with redundant lifting, which yields a high-frequency detail subband and a low-frequency approximation subband. Then, the high-frequency detail subband is filtered in multi-direction before its enhancement by the LSCN algorithm, while the low-frequency approximation subband is directly enhanced by the LSCN algorithm. Finally, the processed sub-images are fused and reconstructed to constitute the enhanced infrared image. In the infrared image of a power transformer, the edge strength, information entropy, peak signal-to-noise ratio, structural similarity and average gradient of the proposed algorithm are at least 10.86%, 14.39%, 19.95%, 7.06% and 6.70% higher than those of other algorithms. The experimental results show that the algorithm not only improves the overall clarity of the infrared image but also strengthens the detail texture and contrast of the image. It has a good infrared image enhancement effect and thus a bright application prospect for power equipment detection.
To address the low signal-to-noise ratio of infrared images and their vulnerability to the impact of the background environment, this study proposes an image enhancement algorithm based on non-separable wavelet based multiscale directional analysis (NSWMDA) and linking synaptic computation network (LSCN). Firstly, the original image is subjected to non-separable wavelet transform (NSWT) with redundant lifting, which yields a high-frequency detail subband and a low-frequency approximation subband. Then, the high-frequency detail subband is filtered in multi-direction before its enhancement by the LSCN algorithm, while the low-frequency approximation subband is directly enhanced by the LSCN algorithm. Finally, the processed sub-images are fused and reconstructed to constitute the enhanced infrared image. In the infrared image of a power transformer, the edge strength, information entropy, peak signal-to-noise ratio, structural similarity and average gradient of the proposed algorithm are at least 10.86%, 14.39%, 19.95%, 7.06% and 6.70% higher than those of other algorithms. The experimental results show that the algorithm not only improves the overall clarity of the infrared image but also strengthens the detail texture and contrast of the image. It has a good infrared image enhancement effect and thus a bright application prospect for power equipment detection.
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Abstract:
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
Abstract:
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
Abstract:
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
Abstract:
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
Abstract:
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
Abstract:
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
Abstract:
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
Abstract:
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
Abstract:
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
Abstract:
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
Abstract:
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
Abstract:
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
Abstract:
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
Abstract:
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
Abstract:
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
Abstract:
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
Abstract:
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Abstract:
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
Abstract:
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
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Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address：4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code：100190
Phone：010-62661041 Fax： Email：csa (a) iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
